Article

Determining the adulteration of spices with Sudan I-II-II-IV dyes by UV-visible spectroscopy and multivariate classification techniques

Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel.lí Domingo s/n Campus Sescelades, E-43007 Tarragona, Spain.
Talanta (Impact Factor: 3.55). 09/2009; 79(3):887-92. DOI: 10.1016/j.talanta.2009.05.023
Source: PubMed

ABSTRACT

We propose a very simple and fast method for detecting Sudan dyes (I, II, III and IV) in commercial spices, based on characterizing samples through their UV-visible spectra and using multivariate classification techniques to establish classification rules. We applied three classification techniques: K-Nearest Neighbour (KNN), Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). A total of 27 commercial spice samples (turmeric, curry, hot paprika and mild paprika) were analysed by chromatography (HPLC-DAD) to check that they were free of Sudan dyes. These samples were then spiked with Sudan dyes (I, II, III and IV) up to a concentration of 5 mg L(-1). Our final data set consisted of 135 samples distributed in five classes: samples without Sudan dyes, samples spiked with Sudan I, samples spiked with Sudan II, samples spiked with Sudan III and samples spiked with Sudan IV. Classification results were good and satisfactory using the classification techniques mentioned above: 99.3%, 96.3% and 90.4% of correct classification with PLS-DA, KNN and SIMCA, respectively. It should be pointed out that with SIMCA, there are no real classification errors as no samples were assigned to the wrong class: they were just not assigned to any of the pre-defined classes.

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    • "Thus, chemical analysis will provide useful information for any questionable samples of dried rhizomes, ground turmeric, turmeric oils or oleoresins, and curcumi-noids/curcumin. Chemical analysis becomes particularly necessary when exotic chemical adulterants such as Sudan dyes (Salmén et al. 1987, Di Anibal et al. 2009, Salmén et al. 1988) Metanil yellow, Orange II and lead chromate are present in turmeric powder which are detected by colorimetric, chromatogarphic or spectrophotometric techniques (Tripathi et al. 2004, 2007). The Thin Layer Chromatography (TLC) fingerprint with a visible pattern of bands provides fundamental data and is typically used to demonstrate the consistency and stability of herbal materials. "
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    ABSTRACT: Turmeric has been recognized as a pharmaceutical crop. It is valuable primarily for essential oil and curcumin content. Chemical composition of the essential oils obtained from the rhizome of turmeric was determined by GC/MS technique. More than 75 compounds were detected and 67 of them were identified. They accounted for 98.59% of essential oil. The essential oil contained 15 monoterpenes (5.58%), 43 sesquiterpenes (84.37%) and 10 nonterpenic components (8.64%). The major constituents were ß-turmeron, a-turmeron, Epi-a-patschutene, ß-sesquiphellandrene, 1,4-dimethyl-2-isobutylbenzene, (±)-dihydro-ar-turmerone, zingiberene, E-a-atlantone and (-)-caryophyllene oxide. Thin layer chromatographic finger printing and quantitative determination of phenolics in acetone extract of commercially available turmeric samples were carried out using Folin-Ciocalteu colorimetric method. Gallic acid was used as the standard for the estimation of phenolics. All the investigated turmeric extracts contained relatively high amount of phenolics.Nepal Journal of Science and Technology Vol. 16, No.1 (2015) pp. 87-94
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    • "In multi-class classification, the discriminant approach is followed more frequently than the class-modeling one. A discriminant classification method which has gained increasing attention in the last years is based on partial least squares (PLS) regression, and it is usually referred to as discriminant PLS (D-PLS) or PLS discriminant analysis (PLS-DA) [6] [7] [8]. In the recent years, a number of attempts have been addressed to develop classmodeling techniques exploiting the advantages offered by the PLS method [9] [10] [11] [12]. "
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    • "Therefore, numerous spectroscopy techniques have been developed to detect the illegal additives in food products [6]. Anibal and coworkers [7] reported a simple and fast method for detection of Sudan dyes (I, II, III and IV) in commercial spices. In this method, samples were characterized through UV–visible spectra, and then the classification rules were established by using multivariate classification techniques. "
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    ABSTRACT: A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.
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